The recent Intecol meeting in London, celebrating the British Ecological Society’s centenary, was perhaps the most Twitter-active (Twinteractive?) conference I’ve been to, with Twitter-only questions at plenaries and plenty of discussion across multiple parallel sessions. One such discussion I dipped into (#ecologyNH) concerned the extent to which a 21st Century ecologist needs to know natural history, a question I’ve been pondering for a while, and one which surfaced again only yesterday in an exchange triggered by Matt Hill (@InsectEcology) and also drawing in Mark Bertness (@mbertness), Ethan White (@ethanwhite) and others.
Now the answer to this of course depends on your particular specialism. If you’re a field ecologist then reliably being able to identify your (perhaps many) study species is clearly critical, and many ecological careers outside of academia require very good identification skills in order to assess habitats, prioritise conservation areas, and so on. But ecology’s a broad field, too broad for any one of us to master all of its subdisciplines, and there are skills other than natural history that are equally useful. In particular, an increasing number of us do a kind of ecology which involves sitting in front of a computer screen and playing with other people’s data. In my case, this is macroecology, trying to understand what determines the distribution and abundance of large groups of species over regional to global scales. Is it really necessary for me to be able to put a face to every species name in my dataset in order to extract the kind of general patterns that interest me?

My view is that the answer to this depends on how we define ‘natural history’. As I’ve posted before, I don’t consider myself much of a natural historian, under the rather narrow definition of being able to key out a large number of species; and I don’t believe this holds me back as an ecologist. But on the other hand, I do think that a ‘feel’ for natural history is important. By this, I mean that understanding in general terms the kinds of organisms you work on, and the sorts of ways in which they interact with each other and with their environment, is likely to enhance your understanding of any dataset, and thus will point you in the direction of interesting questions (and away from silly ones). In the same way, I don’t see why a fisheries minister, for example, should be expected to be able to identify every fish on a fishmonger’s slab in order to make sensible policy decisions; but having some general understanding of fish and fisheries above and beyond numbers on a balance sheet seems important to me.

That’s my general thesis, but if you want some specifics, I believe there are some real practical advantages to be gained from a macroecologist taking the time to learn a bit about the natural history of their system, too. First, we all know how easy it is to introduce errors into a large dataset; being able to relate a species name to a mental image of the kind of organism it represents provides an efficient way to spot obvious errors. This is really just an extension of basic quality control of your data - simple plots to identify outliers and so on. But errors need not be outliers - for instance, if you’re looking at the distribution of body size across a very wide range of species, an obvious mistake, like a 50g cetacean or a 50kg sprat, may not be immediately apparent. One such error was only picked up at the proof stage in this paper, when my coauthor Simon Jennings noticed that one of the figures labelled a 440mm scaldfish which he told me was ‘unrealistically big’, in fact over twice the likely maximum length. He was quite right, as a better knowledge of Irish Sea fish would have told me at the outset; fortunately this time we caught the error on time, and it didn’t affect our conclusions at all.we corrected the figure and did the quick check on all the other species that we should have done at the outset.

Of course, there are more formal ways to check data against known limits, but the point is that a bit of expert knowledge - a basic understanding the range of feasible values for a feature of interest - goes a long way. Having worked on many different taxa, not all of which I have personal experience of, my approach to this is to work with some kind of (preferably colourful) field guide near at hand that I can dip in to to remind myself that points of a graph = organisms in an environment.

Some outliers, of course, remain stubbornly resistant to quality control, and you eventually have to accept that they are real. Here again, a bit of natural history can help you to interpret them and to suggest additional factors that may be important. For instance, I have worked quite a bit on the relationship between the local abundance and regional distribution of species. Such ‘abundance-occupancy’ relationships (AORs) are typically positive, such that locally common species are also regionally widespread. I put it like this: if you drove through Britain, you’d tend to see the same common birds everywhere on your journey, but the rare ones would vary much more from place to place. However, although AORs are well-established as a macroecological generality, there are often outlying species, for instance species with very high local densities but small distributions. Identifying such points (‘Oh, they’re gannets’) and knowing something about them (‘of course, they nest colonially’) can help to explain these anomalies.

Such simple observations - ‘gannets don’t fit the general AOR’ - can then lead to more general predictions - ‘AORs will be different in species that breed colonially’ - that can influence future research directions. In my experience, observations of natural history will frequently suggest new explanations for known patterns, or will lead you to seek out study systems meeting particular criteria in order to test a hunch. A fascination with natural history may lead you to learn about a new ecosystem - deep sea hydrothermal vents, say - which you then start to think may be perfect for testing theories of island biogeography or latitudinal diversity gradients.

You might also start to question models that gloss over natural historical details. On a winter walk in the Peak District I made the very obvious observation that the north-facing side of the steep valley was deeply frosted while the other, only a hundred metres or so distant but south-facing, was really quite pleasantly warm. This got me thinking about how the availability of such microclimates would not be captured in most of the (kilometre scale) GIS climate layers people use in species distribution modelling, yet could be crucial in determining where a species occurs. This is unlikely to have been an original thought, and is not one I’ve followed up, but it emphasises how real world observation can colour your interpretation of computational results.

More generally, real world observation - ‘going one-on-one with a limpet’, as Bob Paine puts it in a nice interview on BioDiverse Perspectives - gives you a sense of the set of plausible explanations for the phenomena that emerge from datasets at scales too large for one person to experience. This in turn leads to a healthy scepticism of hypotheses that fall outside that set. To paraphrase an earlier post of mine, simply plucking patterns from data with no feel for context and contingency is unlikely to lead to the understanding that we crave.

That said, however, there are benefits to be had from putting aside one’s personal experience and being guided, from time to time, by the data. I guess I’m influenced here by working on marine systems, where the human perspective is not a good guide to how organisms perceive their environment. We simply can’t sense the fine structure of many marine habitats, or how dispersal can be limited in what looks like a barrier-less environment. Bob Paine admits as much: directly after the limpet quote, he says “How do you do that with a great white shark or blue whale? There’s this barrier to what I would call natural history.” He goes on to talk about the problems with relying on personal experience when working on systems such as terrestrial forests with very slow dynamics. These long-term, large-scale, hard-to-access systems are, I would argue, exactly where the methods of macroecology and other computational branches of our science come to the fore. It is also, dare I say it, where coordinated observational programmes like NEON can make a real contribution.

But let me finish with perhaps the most important justification for spicing up computer-based ecology with a bit of natural history. We’re supposed to be enjoying ourselves, and for most ecologists surely that means getting out into the field, in whatever capacity - for work or for fun - and wherever it may be, from our back gardens to the back of beyond. My personal view is that doing this whenever you can will make you a better ecologist. But even if I’m wrong, it ought to make you a happier ecologist, and that’s important too.